library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
theme_cowplot2 <- function(...) {
theme_cowplot(font_size = 12, ...) %+replace%
theme(strip.background = element_blank(),
plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
### load all data ---------------------------------
source("_src/global_vars.R")
# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc.rds"))
myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data_wrapper <- function(cluster_res) {
cluster_res <- enquo(cluster_res)
as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(meta_tbl, by = "sample") %>%
rename(cluster = !!cluster_res) %>%
mutate(cluster = as.character(cluster),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}
plot_data <- plot_data_wrapper(louvain_cluster)
WFDC2, CD24, CLDN3, KRT7, KRT8, KRT17, KRT18, KRT19, EPCAM, WT1, CLDN4, MSLN, FOLR1, MUC1
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | Cancer.cell.1 | Cancer.cell.2 | Cancer.cell.3 | Cancer.cell.4 | Cancer.cell.5 | Cancer.cell.6 | Ciliated.cell.1 | Ciliated.cell.2 | Ciliated.cell.3 | Cycling.cancer.cell.1 | Cycling.cancer.cell.2 | doublet.Immune.cell |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | DAPL1 | IGFBP3 | CXCL10 | COL1A1 | CCND1 | FTL | C20orf85 | C5orf49 | ATP5F1E | CENPF | ATAD2 | CD52 |
| 2 | FOLR1 | KRT17 | IFI6 | COL1A2 | CRISP3 | HSPA6 | CAPS | CFAP126 | DEFB1 | HMGB2 | H2AFZ | IGHG1 |
| 3 | SCGB1D2 | KRT7 | IFIT1 | COL3A1 | OVGP1 | MGP | CAPSL | DNALI1 | GNAS | MKI67 | HIST1H4C | IGKC |
| 4 | SCGB2A1 | LCN2 | IFIT2 | DCN | PLCG2 | RACK1 | PIFO | PIFO | GPX3 | PTTG1 | PCLAF | IGLC3 |
| 5 | SNHG19 | MMP7 | IFIT3 | FN1 | PTMS | SCX | TPPP3 | RSPH1 | IMPA2 | TOP2A | TUBA1B | IL7R |
| 6 | S100A10 | ISG15 | SPARC | SNHG9 | ZFAS1 | PTPRC | ||||||
| 7 | S100A9 | MX1 | ||||||||||
| 8 | SLPI | |||||||||||
| 9 | TACSTD2 |
marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc_markers_02.tsv")
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
cluster_n_tbl <- seu_obj$cluster_label %>%
table() %>%
enframe("cluster_label", "cluster_n") %>%
mutate(cluster_nrel = cluster_n/sum(cluster_n))
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
ungroup %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
marker_tbl_annotated <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
left_join(cluster_n_tbl, by = "cluster_label") %>%
select(-cluster) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
arrange(cluster_label, -avg_logFC, p_val_adj)
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated.tsv"))
formattable::formattable(marker_sheet)
| rank | Cancer.cell.1 | Cancer.cell.2 | Cancer.cell.3 | Cancer.cell.4 | Cancer.cell.5 | Cancer.cell.6 | Cycling.cancer.cell.1 | Cycling.cancer.cell.2 | Ciliated.cell.1 | Ciliated.cell.2 | Ciliated.cell.3 | doublet.Immune.cell |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | DAPL1 | S100A9 | ISG15 | COL1A1 | OVGP1 | HSPA6 | CENPF | HIST1H4C | CAPS | CFAP126 | GPX3 | IGKC |
| 2 | SCGB2A1 | KRT17 | CXCL10 | COL3A1 | PLCG2 | FTL | PTTG1 | TUBA1B | C20orf85 | PIFO | GNAS | IGLC3 |
| 3 | SCGB1D2 | LCN2 | IFIT3 | COL1A2 | CRISP3 | RACK1 | UBE2S | PCLAF | TPPP3 | DNALI1 | ATP5F1E | LTB |
| 4 | SNHG19 | TACSTD2 | IFIT1 | FN1 | SNHG9 | ZFAS1 | HMGB2 | ATAD2 | C9orf24 | RSPH1 | DEFB1 | IGHG1 |
| 5 | FOLR1 | IGFBP3 | MX1 | SPARC | CCND1 | SCX | TPX2 | H2AFZ | RSPH1 | C5orf49 | IMPA2 | IL7R |
| 6 | CRABP1 | SLPI | IFI6 | DCN | PTMS | MGP | ASPM | PCNA | C5orf49 | LRRIQ1 | GSTM3 | CD52 |
| 7 | PTGDS | S100A10 | IFIT2 | LUM | PEG10 | IGFBP7 | MKI67 | CKS1B | TMEM190 | FAM183A | PSMA7 | PTPRC |
| 8 | THSD4 | KRT7 | IFI44L | IGFBP7 | DSTN | NPM1 | CCNB1 | TK1 | PIFO | C9orf24 | ANXA4 | GIMAP7 |
| 9 | APOA1 | RARRES1 | PARP14 | COL6A1 | YWHAE | CNBP | CDC20 | MKI67 | FAM183A | CASC1 | FTL | SRGN |
| 10 | GPRC5A | TNFSF10 | COL6A2 | SNHG19 | FKBP4 | TOP2A | TYMS | C1orf194 | C20orf85 | CAPS | IGHG3 | |
| 11 | MMP7 | STAT1 | CCDC80 | APOLD1 | EEF1D | CKS2 | TUBB | CETN2 | ARMC3 | IGFBP7 | IGLC2 | |
| 12 | CRYAB | IFI27 | CTHRC1 | MYL12B | CDV3 | UBE2C | HMGB2 | ODF3B | C1orf194 | PCLAF | BTG1 | |
| 13 | FTH1 | RSAD2 | CALD1 | PTPRA | GAPDH | ARL6IP1 | DUT | TXN | TPPP3 | PLIN2 | CCR7 | |
| 14 | NDRG1 | OAS1 | VIM | WBP11 | OAZ1 | BIRC5 | TOP2A | AGR3 | CFAP45 | PFDN4 | KLF2 | |
| 15 | KRT19 | XAF1 | COL6A3 | SNHG25 | YBX3 | LGALS1 | DEK | CFAP126 | EFCAB1 | C12orf75 | BIRC3 | |
| 16 | S100A11 | GBP1 | NNMT | RBMX | YBX1 | CCNB2 | SMC4 | IGFBP7 | DNAAF1 | RHOBTB3 | CD3D | |
| 17 | VEGFA | SAMD9 | SPARCL1 | EPB41L2 | LTC4S | TUBA1B | MCM7 | CAPSL | ENKUR | CDKN2C | RGS1 | |
| 18 | ANXA1 | IFIH1 | LGALS1 | RBM8A | CYC1 | CEP55 | UBE2C | C11orf88 | CEP126 | BMP7 | SPP1 | |
| 19 | FTL | GBP4 | MGP | PABPC4 | NOP53 | CDKN3 | HELLS | MORN2 | HYDIN | EIF3K | IL32 | |
| 20 | SLC2A1 | PSMB9 | TAGLN | METAP2 | EEF2 | NUSAP1 | CDK1 | TUBB4B | PPIL6 | XAGE3 | CD48 | |
| 21 | SOX4 | DDX58 | MMP11 | AMD1 | EDN1 | JPT1 | MCM3 | TUBA1A | CAPSL | DSTN | FYB1 | |
| 22 | S100A6 | LY6E | VCAN | STRAP | EIF4A2 | H2AFZ | CENPF | LRRIQ1 | ZMYND10 | MSRB1 | TRBC2 | |
| 23 | TFPI2 | ISG20 | ACTA2 | BRD9 | TSTA3 | NMU | STMN1 | FOXJ1 | TEKT2 | EIF1 | IKZF1 | |
| 24 | PI3 | OAS3 | RARRES2 | CREBZF | PRELID1 | KPNA2 | MYBL2 | ZMYND10 | CFAP43 | ATP5PO | CORO1A | |
| 25 | C15orf48 | HLA-B | COL5A2 | MYL6B | SLU7 | HMMR | CLSPN | DNAAF1 | DYDC2 | RDH10 | CD3G | |
| 26 | NEAT1 | CXCL11 | AEBP1 | PPT1 | CANX | PTMS | UBE2T | C9orf116 | CFAP44 | COX6B1 | B2M | |
| 27 | S100A2 | RNF213 | POSTN | SNRPB | CTTNBP2 | HMGN2 | NASP | EFCAB1 | CDC20B | SDC2 | CD2 | |
| 28 | TNFRSF12A | LAP3 | PLA2G2A | MYL12A | TTC1 | ECT2 | MCM4 | EFHC1 | CFAP54 | SFTA2 | GMFG | |
| 29 | C19orf33 | IFI16 | ERP27 | TMCO1 | CLINT1 | SMC4 | SMC2 | DNALI1 | FAM81B | ORM1 | RIPOR2 | |
| 30 | TCIM | TAP1 | NBL1 | SNRPB2 | TMEM238 | HMGB1 | NUSAP1 | DYDC2 | SPATA17 | CNTD2 | ETS1 | |
| 31 | TAGLN | IDO1 | COL8A1 | NAP1L1 | COX4I1 | CENPE | HMGB1 | IK | TEKT1 | HOXB-AS3 | GIMAP4 | |
| 32 | MAL2 | HLA-A | TIMP3 | ST13 | ZNF787 | STMN1 | RAD51AP1 | CEP126 | SPEF1 | PTH1R | HCST | |
| 33 | LGALS3 | PLSCR1 | LRRC75A | PTX3 | CLTB | ANP32E | ZWINT | SNTN | CFAP46 | CYS1 | TRAC | |
| 34 | NUPR1 | HERC5 | IGFBP4 | MIR4458HG | HIGD2A | CKS1B | TPX2 | AL357093.2 | DNAH7 | GLRX | TRBC1 | |
| 35 | ANXA2 | IFI44 | CDH6 | WDR70 | NACA | KIF20B | DNMT1 | ROPN1L | CFAP73 | EEF1A2 | TMSB4X | |
| 36 | CST6 | B2M | MMP2 | WDR45B | UQCRB | RAD21 | RRM2 | MLF1 | ZBBX | ERVMER34-1 | CXCR4 | |
| 37 | EMP1 | HLA-C | BGN | PA2G4 | ATP5PB | NUCKS1 | E2F1 | PSENEN | ROPN1L | CA2 | LAPTM5 | |
| 38 | IL32 | OAS2 | TPM1 | NMT1 | RSPO3 | HMGB3 | CENPW | ERICH3 | EFCAB10 | LINC02532 | CD3E | |
| 39 | SQSTM1 | BST2 | IGFBP6 | TTYH1 | RSL1D1 | TROAP | RANBP1 | CCDC170 | WDR38 | NCCRP1 | C1QB | |
| 40 | CAST | WARS | C1R | VIM | CYHR1 | DLGAP5 | MAD2L1 | TEKT2 | SPAG8 | LAMB4 | LCP1 | |
| 41 | MUC4 | SAMD9L | SPON2 | KRT81 | UBE2D2 | AURKA | TMPO | PERP | DRC1 | BICC1 | ARHGDIB | |
| 42 | TGM2 | EIF2AK2 | COL5A1 | IGFBP3 | S100A1 | H2AFV | CENPX | SPA17 | CFAP157 | MIOX | CD37 | |
| 43 | SAT1 | UBE2L6 | KRT7 | NSA2 | SRP72 | TUBB4B | FANCI | C9orf135 | RSPH4A | ORM2 | TYROBP | |
| 44 | CLDN4 | MX2 | HTRA1 | HSP90B1 | SSB | CENPA | TMEM106C | LRRC23 | WDR49 | CPNE8 | CCL4 | |
| 45 | FAM3C | OASL | CAV1 | HMGB1 | PUF60 | NCAPD2 | DHFR | CFAP45 | C7orf57 | NID2 | EMP3 | |
| 46 | BIRC3 | IFI35 | SYT8 | TMA7 | BTF3 | CENPW | PRKDC | ARMC3 | DYNLRB2 | AC022784.1 | LSP1 | |
| 47 | LDHA | IFITM3 | C1S | ANO1 | UQCRH | TUBA1C | HMGN2 | WDR54 | WDR63 | SLCO4A1 | IGHG4 | |
| 48 | CAMK2N1 | HLA-E | SERPINF1 | PEG3 | EIF3E | NEK2 | BIRC5 | MNS1 | MAP3K19 | CR1L | TRAF3IP3 | |
| 49 | ITGB8 | RARRES3 | ANXA2 | CCND2 | MAP2K2 | NUF2 | DTYMK | TEKT1 | C6orf118 | RBP7 | STK17B | |
| 50 | MUC16 | NUPR1 | CFH | CD177 | LINC02308 | MAD2L1 | ASPM | TUBA4B | DNAH12 | PCAT19 | ACAP1 |
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:20, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes))
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
scale_color_manual(values = clrs$cluster_label[[coi]]) +
#facet_wrap(~cluster_label) +
ggtitle("Sub cluster")
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
# ## compute expression module scores
# for (i in 1:length(signature_modules)) {
# seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
# seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
# seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
# print(paste(names(signature_modules)[i], "DONE"))
# }
#
# ## compute progeny scores
# progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
# as.matrix %>%
# progeny %>%
# as.data.frame %>%
# as.list
#
# names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
#
# for (i in 1:length(progeny_list)) {
# seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
# col.name = names(progeny_list)[i])
# }
#
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
labs(fill = "Cluster", y = "Fraction [%]", x = "")
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
labs(fill = "Cluster", y = "# cells", x = "")
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-01-20
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
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